{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ipdreamer-appearance-controllable-3d-object","title":"IPDreamer: Appearance-Controllable 3D Object Generation with Complex Image Prompts","arxiv_id":"2310.05375","date":"2023-10-09","proceeding":null,"authors":["Bohan Zeng","Shanglin Li","Yutang Feng","Ling Yang","Hong Li","Sicheng Gao","Jiaming Liu","Conghui He","Wentao Zhang","Jianzhuang Liu","Baochang Zhang","Shuicheng Yan"],"abstract":"Recent advances in 3D generation have been remarkable, with methods such as DreamFusion leveraging large-scale text-to-image diffusion-based models to guide 3D object generation. These methods enable the synthesis of detailed and photorealistic textured objects. However, the appearance of 3D objects produced by such text-to-3D models is often unpredictable, and it is hard for single-image-to-3D methods to deal with images lacking a clear subject, complicating the generation of appearance-controllable 3D objects from complex images. To address these challenges, we present IPDreamer, a novel method that captures intricate appearance features from complex $\\textbf{I}$mage $\\textbf{P}$rompts and aligns the synthesized 3D object with these extracted features, enabling high-fidelity, appearance-controllable 3D object generation. Our experiments demonstrate that IPDreamer consistently generates high-quality 3D objects that align with both the textual and complex image prompts, highlighting its promising capability in appearance-controlled, complex 3D object generation. Our code is available at https://github.com/zengbohan0217/IPDreamer.","url_abs":"https://arxiv.org/abs/2310.05375v6","url_pdf":"https://arxiv.org/pdf/2310.05375v6.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ipdreamer-appearance-controllable-3d-object","repo_url":"https://github.com/zengbohan0217/ipdreamer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-generation","task_name":"3D Generation"},{"task_slug":"image-to-3d","task_name":"Image to 3D"},{"task_slug":"object","task_name":"Object"},{"task_slug":"text-to-3d","task_name":"Text to 3D"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2310.05375","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}